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The R package otu2ot for implementing the entropy decomposition of nucleotide variation in sequence data.

Ramette A, Buttigieg PL - Front Microbiol (2014)

Bottom Line: The aim of this implementation is to facilitate the integration of computational routines, interactive statistical analyses, and visualization into a single framework.These enhancements are especially useful for large datasets, where a manual screening of entropy analysis results and the creation of a full set of OTs may not be feasible.The package and procedures are illustrated by several tutorials and examples.

View Article: PubMed Central - PubMed

Affiliation: HGF-MPG Group for Deep Sea Ecology and Technology, Max Planck Institute for Marine Microbiology Bremen, Germany.

ABSTRACT
Oligotyping is a novel, supervised computational method that classifies closely related sequences into "oligotypes" (OTs) based on subtle nucleotide variation (Eren et al., 2013). Its application to microbial datasets has helped reveal ecological patterns which are often hidden by the way sequence data are currently clustered to define operational taxonomic units (OTUs). Here, we implemented the OT entropy decomposition procedure and its unsupervised version, Minimal Entropy Decomposition (MED; Eren et al., 2014c), in the statistical programming language and environment, R. The aim of this implementation is to facilitate the integration of computational routines, interactive statistical analyses, and visualization into a single framework. In addition, two complementary approaches are implemented: (1) An analytical method (the broken stick model) is proposed to help identify OTs of low abundance that could be generated by chance alone and (2) a one-pass profiling (OP) method, to efficiently identify those OTUs whose subsequent oligotyping would be most promising to be undertaken. These enhancements are especially useful for large datasets, where a manual screening of entropy analysis results and the creation of a full set of OTs may not be feasible. The package and procedures are illustrated by several tutorials and examples.

No MeSH data available.


One-Pass (OP) analysis of HGB_0013_GXJPMPL01A3OQX.fasta. (A) Shannon entropy profile, (B) nucleotide composition of the 5 high-entropy positions, (C) Relative abundance of each OT obtained by OP, (D) raw compositional table.
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Figure 3: One-Pass (OP) analysis of HGB_0013_GXJPMPL01A3OQX.fasta. (A) Shannon entropy profile, (B) nucleotide composition of the 5 high-entropy positions, (C) Relative abundance of each OT obtained by OP, (D) raw compositional table.

Mentions: OP analysis of the same alignment file indicated 5 positions associated with high Shannon entropy values (Figures 3A,B; Tutorial 2). Further concatenation and binning of the sequence data led to 4 dominant OTs (Figure 3C) out of the 17 OTs generated by OP. Most of the rarer OTs were, in fact, singletons (Figure 3D). Subsequent BSM filtering (Figure 4A) led to a compositional table (Figure 4B) very similar to the one obtained by MED followed by BSM filtering (Figure 2D). Despite those similar plots, a number of differences may be observed which require careful investigation to fully compare the results produced by OP and MED (Tutorial 3).


The R package otu2ot for implementing the entropy decomposition of nucleotide variation in sequence data.

Ramette A, Buttigieg PL - Front Microbiol (2014)

One-Pass (OP) analysis of HGB_0013_GXJPMPL01A3OQX.fasta. (A) Shannon entropy profile, (B) nucleotide composition of the 5 high-entropy positions, (C) Relative abundance of each OT obtained by OP, (D) raw compositional table.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4231947&req=5

Figure 3: One-Pass (OP) analysis of HGB_0013_GXJPMPL01A3OQX.fasta. (A) Shannon entropy profile, (B) nucleotide composition of the 5 high-entropy positions, (C) Relative abundance of each OT obtained by OP, (D) raw compositional table.
Mentions: OP analysis of the same alignment file indicated 5 positions associated with high Shannon entropy values (Figures 3A,B; Tutorial 2). Further concatenation and binning of the sequence data led to 4 dominant OTs (Figure 3C) out of the 17 OTs generated by OP. Most of the rarer OTs were, in fact, singletons (Figure 3D). Subsequent BSM filtering (Figure 4A) led to a compositional table (Figure 4B) very similar to the one obtained by MED followed by BSM filtering (Figure 2D). Despite those similar plots, a number of differences may be observed which require careful investigation to fully compare the results produced by OP and MED (Tutorial 3).

Bottom Line: The aim of this implementation is to facilitate the integration of computational routines, interactive statistical analyses, and visualization into a single framework.These enhancements are especially useful for large datasets, where a manual screening of entropy analysis results and the creation of a full set of OTs may not be feasible.The package and procedures are illustrated by several tutorials and examples.

View Article: PubMed Central - PubMed

Affiliation: HGF-MPG Group for Deep Sea Ecology and Technology, Max Planck Institute for Marine Microbiology Bremen, Germany.

ABSTRACT
Oligotyping is a novel, supervised computational method that classifies closely related sequences into "oligotypes" (OTs) based on subtle nucleotide variation (Eren et al., 2013). Its application to microbial datasets has helped reveal ecological patterns which are often hidden by the way sequence data are currently clustered to define operational taxonomic units (OTUs). Here, we implemented the OT entropy decomposition procedure and its unsupervised version, Minimal Entropy Decomposition (MED; Eren et al., 2014c), in the statistical programming language and environment, R. The aim of this implementation is to facilitate the integration of computational routines, interactive statistical analyses, and visualization into a single framework. In addition, two complementary approaches are implemented: (1) An analytical method (the broken stick model) is proposed to help identify OTs of low abundance that could be generated by chance alone and (2) a one-pass profiling (OP) method, to efficiently identify those OTUs whose subsequent oligotyping would be most promising to be undertaken. These enhancements are especially useful for large datasets, where a manual screening of entropy analysis results and the creation of a full set of OTs may not be feasible. The package and procedures are illustrated by several tutorials and examples.

No MeSH data available.